我听过不少人凭借爱好开源了自己的项目后, 却对 issue 太乱感到困扰, 甚至想干脆直接禁用 issue. 其实, 任何项目达到一定规模后, 如果不对 issue 进行适当管理, 都会使 issue 信噪比过低, 失去原本的功能.

这篇文章主要从 maintainer 的角度说说, 在具备规模的项目中管理 issue 的一些方法和原则.

报告错误 / 报 bug 是用户与开发者间最常见的一类交流, 也是常见的 github issue. 但是很多用户并不会科学的报 bug, maintainer 对此也缺乏引导. 因此这篇文章讨论如何科学的报 bug.

相比传统的邮件列表 / bugzilla/sourceforge 等开源平台, github 把开源社区交流的成本 / 门槛降的很低, 因此交流的质量也常常随之下降.

我计划写几篇文章, 从 用户 (User) 和 维护者 (Maintainer) 两者的角度写写开源社区中如何使用 issue/PR 进行沟通, 希望能够:

• 让普通用户学会提问, 学会以 maintainer 更容易接受的方式使用 issue/PR 做贡献.
• 给 maintainer 提供一些管理 issue/PR 的经验, 不要再为 issue 头疼.

In large systems, logs can be terrifying: they are huge in volume, and hard to understand. This note lists some suggestions and common misuse of Python's logging module, with the aim of:

• Reduce redundant logs & spams from libraries.
• Allow more control of logging behaviors.
• Make logs more informative to users.

延续 上一篇文章, 再说一说怎么科学的在 paper 里做 ablations.

Technically, an image is a function that maps a continuous domain, e.g. a box , to intensities such as (R, G, B). To store it on computer memory, an image is discretized to an array array[H][W], where each element array[i][j] is a pixel.

How does discretization work? How does a discrete pixel relate to the abstract notion of the underlying continuous image? These basic questions play an important role in computer graphics & computer vision algorithms.

This article discusses these low-level details, and how they affect our CNN models and deep learning libraries. If you ever wonder which resize function to use or whether you should add/subtract 0.5 or 1 to some pixel coordinates, you may find answers here. Interestingly, these details have contributed to many accuracy improvements in Detectron and Detectron2.

这几年来, 从 FAIR 的几位大佬身边学习到的最多的是对待 research 的态度. 因此说说写 paper 和做实验的体会.

实验与 claims

实验是为了证明或强化文章里给出的 claim/hypothesis 的.

Ross ICCV 2019 tutorial 最后谈了谈怎么写 paper. 第 126 页说, 文章中所有的 claim, 理想情况下都应该要么是文献中已有的 claim, 要么是有实验能够证明的 claim.

开源工具链里有很多陈年小 "feature", 最初由于各种原因 (例如作为 workaround) 实现了之后, 即使语义模糊或设计不合理, 也因为兼容性被留到了今天.

吐个小槽. 很久以前有次我在知乎上的一个回答里夸了 TensorFlow 1.x, 然后被人抱怨说 graph mode 写不了 IfElse 不能忍.

然而, PyTorch 就可以写 IfElse 了?

TL;DR: How to find out if your favorite deep learning library is occasionally giving you wrong results? Such bugs happen from time to time, and are extremely difficult to notice, report, and debug.